Question

我正试图抓住与非行共同开展的行动,我谨以<条码>@ndb.tasklet作为我的一些工作。

The simple example would be string_id generation in the overridden get_or_insert_async

这是否正确? 在这里可以改进什么?

@classmethod
@ndb.tasklet
def get_or_insert_async(cls, *args):
    id = cls.make_string_id(*args) 
    model = yield super(MyModel, cls).get_or_insert_async(id)
    raise ndb.Return(model)

另一个例子就是以狂热的方式 lo。这是否正确?

@classmethod
@ndb.tasklet
def do_stuff(cls, some_collection):

    @ndb.tasklet
    def internal_tasklet(data):
        do_some_long_taking_stuff(data)
        id = make_stuff_needed_for_id(data)
        model = yield cls.get_or_insert_async(id)
        model.long_processing(data)
        yield model.put_async()
        raise ndb.Return(None)

    for data in some_collection:
        # will it parallelise internal_tasklet execution? 
        yield internal_tasklet(data)

    raise ndb.Return(None)

EDIT:

按照整个概念的理解,<代码>yields>在此提供Future物体,然后以平行(尽可能)方式收集并按顺序执行。我是否正确?

在Nick s hint(是你指的是什么)之后:

@classmethod
@ndb.tasklet
def do_stuff(cls, some_collection):

    @ndb.tasklet
    def internal_tasklet(data):
        do_some_long_taking_stuff(data)
        id = make_stuff_needed_for_id(data)
        model = yield cls.get_or_insert_async(id)
        model.long_processing(data)
        raise ndb.Return(model)                # change here

    models = []
    for data in some_collection:
        # will it parallelise internal_tasklet execution? 
        m = yield internal_tasklet(data)       # change here
        models.appedn(m)                       # change here

    keys = yield ndb.put_multi_async(models)   # change here
    raise ndb.Return(keys)                     # change here

EDIT:

新订正本......

@classmethod
@ndb.tasklet
def do_stuff(cls, some_collection):

    @ndb.tasklet
    def internal_tasklet(data):
        do_some_long_taking_stuff(data)
        id = make_stuff_needed_for_id(data)
        model = yield cls.get_or_insert_async(id)
        model.long_processing(data)
        raise ndb.Return(model)                

    futures = []
    for data in some_collection:
        # tasklets won t run in parallel but while 
        # one is waiting on a yield (and RPC underneath)  
        # the other will advance it s execution 
        # up to a next yield or return
        fut = internal_tasklet(data))          # change here
        futures.append(fut)                    # change here

    Future.wait_all(futures)                   # change here

    models = [fut.get_result() for fut in futures]
    keys = yield ndb.put_multi_async(models)   # change here
    raise ndb.Return(keys)                     # change here

Answer 1

如果你们都想做的话,就不必使用任务手册,而是用不同的论点来说一句话——正如以下那样:

def get_or_insert_async(cls, *args):
  id = cls.make_string_id(*args)
  return super(MyModel, cls).get_or_insert_async(id)

I d be cautious about this for several reasons, though: You re changing the meaning of a built in function, which is usually a bad idea, you re changing the signature (positional arguments but no keyword arguments), and you re not passing extra arguments through to the original function.

就你的第二个例子而言,在某个时候产生一件事情,就会迫使非行等待完成,结果与等待一样。相反,为收集工作的每一部分履行任务职能,然后同时等到所有方面(通过在名单上要求产出)。

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